Collaborative Research: From genotype to fluxome to a comprehensive kinetic model of Escherichia coli

合作研究：从基因型到通量组再到大肠杆菌的综合动力学模型

• 批准号: 1616332
• 负责人: Maciej Antoniewicz
• 金额: $ 90万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1616332&HistoricalAwards=false
• 关键词: Collaborative; Research; genotype; fluxome; comprehensive

Elucidating and quantifying the complexities of biological systems is of fundamental and practical interest to synthetic biologists and metabolic engineers. A useful approach to unraveling biological complexity is to observe the biological system under perturbed conditions, for example, by removing key cellular components such as genes. In this study, responses to gene knockouts in the model microorganism Escherichia coli will be investigated. By systematically measuring cellular responses to the removal of specific genes in central carbon metabolism a high-quality data set will be generated that will then be used to construct a predictive kinetic model. In addition to having significant value in advancing fundamental biological sciences, this model will provide a valuable new toolkit for industrial biotechnology. Additionally, this model will form the basis for the development of new and enhanced educational tools that will introduce high-school and college students to the biological systems at an early age. Developing a predictive kinetic model of cellular metabolism has been the desire of the scientific community for many decades. However, limitations in experimental approaches for measuring precise metabolic fluxes and the daunting task of estimating a large number of kinetic parameters in models of cellular metabolism have prevented its realization. In recent years, a number of major breakthroughs have been achieved that allow, for the first time, this long-standing task to be accomplished successfully. First, new experimental approached based on parallel 13C-labeling experiments have greatly improved the precision and accuracy of flux measurements. Second, the ensemble kinetic modeling approach was developed and validated for biological systems, which allows systematic construction of comprehensive kinetic models of cellular metabolism while limiting problems of parameter identifiability that have been a major concern in the past and have limited previous modeling approaches. In this project, metabolic flux redistribution in response to gene knockouts will be comprehensively assessed for a large number of E. coli knockout strains, both single gene and double knockouts, enabling the development of a comprehensive kinetic model of central metabolism. The proposed work will add to the scientific knowledge by: (1) generating extensive high-quality experimental data on metabolic fluxes for a large number of E. coli mutant strains using state-of-the-art 13C-flux analysis methods; (2) providing a comprehensive kinetic model of cellular metabolism for E. coli, an important academic and industrial microbe; (3) developing and implementing best practices, standards, procedures and tools for performing, documenting and sharing of 13C-flux analysis results and kinetic models; (4) building a framework to integrate multiple omics data sets within the ensemble modeling framework; (5) generating new knowledge and insights about the regulation of E. coli metabolism under glucose-rich and glucose-limited conditions.This award was co-funded by the Systems and Synthetic Biology (SSB) program in the Molecular and Cellular Biosciences (MCB) Division in the Biological Sciences Directorate and the Biotechnology and Biochemical Engineering (BBE) program of the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET) in the Engineering Directorate.

阐明和量化生物系统的复杂性是合成生物学家和代谢工程师的基本和实际兴趣。揭示生物复杂性的一个有用的方法是观察受干扰条件下的生物系统，例如，通过去除关键的细胞成分，如基因。在这项研究中，将研究模式微生物大肠杆菌对基因敲除的反应。通过系统地测量细胞对去除中心碳代谢中特定基因的反应，将生成高质量的数据集，然后用于构建预测动力学模型。除了在推进基础生物科学方面具有重要价值外，该模型还将为工业生物技术提供有价值的新工具包。此外，该模型将为开发新的和增强的教育工具奠定基础，这些工具将向高中生和大学生介绍早期的生物系统。开发细胞代谢的预测动力学模型是科学界几十年来的愿望。然而，测量精确代谢通量的实验方法的局限性和估计细胞代谢模型中大量动力学参数的艰巨任务阻碍了它的实现。近年来，取得了一些重大突破，使这一长期任务第一次得以成功完成。首先，基于平行13c标记实验的新实验方法大大提高了通量测量的精度和准确度。其次，针对生物系统开发并验证了集合动力学建模方法，该方法允许系统地构建细胞代谢的综合动力学模型，同时限制了过去主要关注的参数可识别性问题，并且限制了以前的建模方法。本项目将对大量大肠杆菌敲除菌株（包括单基因敲除菌株和双基因敲除菌株）进行基因敲除后代谢通量重分布的综合评估，建立中心代谢的综合动力学模型。该工作将通过以下方式增加科学知识：(1)使用最先进的13c -通量分析方法生成大量大肠杆菌突变菌株代谢通量的大量高质量实验数据；(2)为重要的学术和工业微生物大肠杆菌提供了细胞代谢的综合动力学模型；(3)制定和实施执行、记录和共享13c通量分析结果和动力学模型的最佳实践、标准、程序和工具；(4)构建框架，在集成建模框架内集成多个组学数据集；(5)对富糖和限糖条件下大肠杆菌代谢调控产生新的认识和见解。该奖项由生物科学理事会分子和细胞生物科学（MCB）部门的系统和合成生物学（SSB）项目以及工程理事会化学、生物工程、环境和运输系统（CBET）部门的生物技术和生化工程（BBE）项目共同资助。